The present disclosure relates to the use of image analysis in the field of histology to identify glandular and tubule glandular regions in breast cancer tissue.
Histological grading is an important step in breast cancer prognosis. In the popular Nottingham Histologic Score (NHS) system for breast cancer grading (c.f. Breast Cancer Research and Treatment, 1992, Volume 22, Issue 3, pp 207-219, The Nottingham prognostic index in primary breast cancer, Marcus H. Galea et. al.), the pathologist analyzes tissue for tubule formation, nuclear pleomorphism and mitotic activity in the tumor regions and assigns a score of 1-3 for each factor. The scores from these three factors are added to give a final score, ranging from 3-9 to grade the cancer.
Tubule score is traditionally calculated by manually estimating the percentage of glandular regions in the tumor that form tubules, which is a time-consuming and subjective process. Others have attempted to automate this process.
For example, Dalle et al. proposed detecting tubules by: (i) segmenting the neoplasm regions (nuclei regions), (ii) applying different morphological operations to the neoplasm regions to segment the blob structures, and (iii) classifying blobs that contain white regions (lumina) as tubule formation.
Maqlin et al. proposed detecting tubules by: (i) segmenting the tissue image into stroma, nuclei and lumen using k-means clustering technique, (ii) finding nuclei boundary using the level set method, (iii) finding the nearest nuclei to each lumen and (iv) evaluating the distance between nuclei surrounding the lumen to estimate the evenness of the nuclei distribution around the lumen, which is used to identify the true tubules from the other white regions. In both the Dalle and Maqlin methods, tubules were identified by connecting each lumen to its closest nuclei. Since these methods only associate the lumen and its closest nuclei, they cannot handle cases in which a lumen is surrounded by multiple layers of nuclei, which can adversely affect the estimation of tubule percentage. Additionally, these methods rely on analysis of images that represent only a small segment of the whole slide tissue, which is not ideal. To our knowledge, no current methods are available that can address both of these deficiencies.